793. Expert Panel Consensus Ranking of Comorbid Conditions Causally Related to Clostridioides difficile Infection

Abstract

BackgroundNumerous studies have identified comorbidities that are associated with Clostridioides difficile infection (CDI), but current CDC and CMS models for risk adjusting hospital CDI rates do not include patient comorbid conditions. Incorporating patient-level data could improve CDI risk adjustment, but comorbidities would need to be easily electronically available for widescale implementation. Ideally, they would also be causally related to CDI — i.e., true risk factors, not confounders — to facilitate more unbiased inter-hospital comparisons. The current study aimed to determine which comorbid conditions are causally related to CDI based upon expert consensus.MethodsWe used Delphi methodology to administer an iterative, two-round survey with an intervening teleconference, to eight infectious disease experts. Experts evaluated 40 comorbid conditions included in Charlson and Elixhauser comorbidity indices (and thus validated for electronic capture through administrative data), as well as other comorbidities commonly associated with CDI. Experts rated comorbid conditions from 1 (not at all related) to 5 (strongly related), based upon perceived relatedness with CDI. To assign causal relatedness, the following criteria had to be met at the end of round two: 1) majority (> 50%) of experts rating the condition at 3 (somewhat related) or higher; 2) inter-quartile range (IQR) < = 1; and 3) standard deviation (SD) < = 1.Results8/40 (20%) comorbid conditions were ranked as causally related to CDI, including patient age, three malignancy comorbidities, two transplant-related comorbidities, HIV/AIDS, and inflammatory bowel disease. A further 18/40 (45%) qualified as indeterminately related, and 14/40 (35%) were ranked as not causally related to CDI (Table). Three of the eight causally related factors were not components of Elixhauser or Charlson indices.Table ConclusionWe identified comorbid conditions that may be appropriate candidates to consider for inclusion in patient-level risk adjustment models. Some causal factors did not originate from established comorbidity indices. Thus, future work to validate electronic capture of these conditions could further reduce barriers to risk-adjustment implementation.Disclosures All Authors: No reported disclosures